{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/Fine_tuning_LayoutLMForTokenClassification_on_FUNSD.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ngqdEv0rP01q"
   },
   "source": [
    "## Introduction\n",
    "\n",
    "In this notebook, we are going to fine-tune the LayoutLM model by Microsoft Research on the [FUNSD](https://guillaumejaume.github.io/FUNSD/) dataset, which is a collection of annotated form documents. The goal of our model is to learn the annotations of a number of labels (\"question\", \"answer\", \"header\" and \"other\") on those forms, such that it can be used to annotate unseen forms in the future.\n",
    "\n",
    "* Original LayoutLM paper: https://huggingface.co/papers/1912.13318\n",
    "\n",
    "* Original FUNSD paper: https://huggingface.co/papers/1905.13538\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6K4S2s33ebY0"
   },
   "source": [
    "## Install libraries\n",
    "\n",
    "Currently you have to first install the `unilm` package, and then the `transformers` package (which updates the outdated `transformers` package that is included in the `unilm` package). The reason we also install the `unilm` package is because we need its preprocessing files. I've forked it, and removed some statements which introduced some issues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "5cngOTr6SqEf",
    "outputId": "6c7a2f76-682b-4f93-a3db-59ab010e5ffe"
   },
   "outputs": [],
   "source": [
    "! rm -r unilm\n",
    "! git clone https://github.com/microsoft/unilm.git"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RGMkEG5aRB0D"
   },
   "source": [
    "## Getting the data\n",
    "\n",
    "Here we download the data of the [FUNSD dataset](https://guillaumejaume.github.io/FUNSD/) from the web. This results in a directory called \"data\" being created, which has 2 subdirectories, one for training and one for testing. Each of those has 2 subdirectories in turn, one containing the images as png files and one containing the annotations in json format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DTFnEZagQm4v",
    "outputId": "97ce03ba-a6bb-4444-8eba-77eceece44e0"
   },
   "outputs": [],
   "source": [
    "! wget https://guillaumejaume.github.io/FUNSD/dataset.zip\n",
    "! unzip dataset.zip && mv dataset data && rm -rf dataset.zip __MACOSX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UrNMR64LsJXm"
   },
   "source": [
    "Let's take a look at a training example. For this, we are going to use PIL (Python Image Library)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "eG-eGcj3sNPs",
    "outputId": "69ead0ea-15d6-4d5e-af61-a99a7533d31b"
   },
   "outputs": [
    {
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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=762x1000>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from PIL import Image, ImageDraw, ImageFont\n",
    "import os\n",
    "\n",
    "base_path = \"./data\"\n",
    "\n",
    "image = Image.open(os.path.join(base_path, \"training_data/images/0000971160.png\"))\n",
    "image = image.convert(\"RGB\")\n",
    "image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uAVffmnZyUvw"
   },
   "source": [
    "Now let's plot its corresponding annotations. Basically, if you type `data['form']`, you get a list of all general annotations. Each general annotation has a label, a bounding box, and one or more words, which in also have their own bounding box. The bounding boxes are in [xleft, ytop, xright, ybottom] format.\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JPKkuJQ4sdZc",
    "outputId": "c95bf306-98bb-4480-cc6b-ebb3aea548b3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'box': [292, 91, 376, 175], 'text': 'R&D', 'label': 'other', 'words': [{'box': [292, 91, 376, 175], 'text': 'R&D'}], 'linking': [], 'id': 0}\n",
      "{'box': [219, 316, 225, 327], 'text': ':', 'label': 'question', 'words': [{'box': [219, 316, 225, 327], 'text': ':'}], 'linking': [], 'id': 1}\n",
      "{'box': [95, 355, 169, 370], 'text': 'Suggestion:', 'label': 'question', 'words': [{'box': [95, 355, 169, 370], 'text': 'Suggestion:'}], 'linking': [[2, 16]], 'id': 2}\n",
      "{'box': [482, 268, 518, 282], 'text': 'Date:', 'label': 'question', 'words': [{'box': [482, 268, 518, 282], 'text': 'Date:'}], 'linking': [[3, 12]], 'id': 3}\n",
      "{'box': [511, 309, 570, 323], 'text': 'Licensee', 'label': 'answer', 'words': [{'box': [511, 309, 570, 323], 'text': 'Licensee'}], 'linking': [[13, 4]], 'id': 4}\n",
      "{'box': [211, 651, 217, 662], 'text': '', 'label': 'question', 'words': [{'box': [211, 651, 217, 662], 'text': ''}], 'linking': [], 'id': 5}\n",
      "{'box': [461, 605, 483, 619], 'text': 'Yes', 'label': 'question', 'words': [{'box': [461, 605, 483, 619], 'text': 'Yes'}], 'linking': [[19, 6]], 'id': 6}\n",
      "{'box': [545, 603, 563, 617], 'text': 'No', 'label': 'question', 'words': [{'box': [545, 603, 563, 617], 'text': 'No'}], 'linking': [[19, 7]], 'id': 7}\n",
      "{'box': [525, 904, 641, 926], 'text': '597005708', 'label': 'other', 'words': [{'box': [525, 904, 641, 926], 'text': '597005708'}], 'linking': [], 'id': 8}\n",
      "{'text': 'R&D QUALITY IMPROVEMENT SUGGESTION/ SOLUTION FORM', 'box': [256, 201, 423, 230], 'linking': [], 'label': 'header', 'words': [{'text': 'R&D', 'box': [257, 203, 279, 214]}, {'text': 'QUALITY', 'box': [285, 203, 334, 216]}, {'text': 'IMPROVEMENT', 'box': [341, 201, 418, 211]}, {'text': 'SUGGESTION/', 'box': [256, 215, 324, 229]}, {'text': '', 'box': [324, 216, 332, 230]}, {'text': 'SOLUTION', 'box': [331, 214, 387, 228]}, {'text': 'FORM', 'box': [395, 215, 423, 228]}], 'id': 9}\n",
      "{'text': 'Name / Phone Ext. :', 'box': [89, 272, 204, 289], 'linking': [[10, 11]], 'label': 'question', 'words': [{'text': 'Name', 'box': [89, 274, 118, 289]}, {'text': '/', 'box': [117, 274, 127, 288]}, {'text': 'Phone', 'box': [128, 274, 163, 289]}, {'text': 'Ext.', 'box': [169, 272, 196, 287]}, {'text': ':', 'box': [196, 274, 204, 288]}], 'id': 10}\n",
      "{'text': 'M. Hamann P. Harper, P. Martinez', 'box': [215, 271, 451, 287], 'linking': [[10, 11]], 'label': 'answer', 'words': [{'text': 'M.', 'box': [215, 272, 230, 287]}, {'text': 'Hamann', 'box': [237, 272, 287, 286]}, {'text': 'P.', 'box': [293, 272, 307, 286]}, {'text': 'Harper,', 'box': [314, 274, 363, 285]}, {'text': 'P.', 'box': [370, 272, 384, 285]}, {'text': 'Martinez', 'box': [390, 271, 451, 282]}], 'id': 11}\n",
      "{'text': '9/ 3/ 92', 'box': [543, 264, 590, 279], 'linking': [[3, 12]], 'label': 'answer', 'words': [{'text': '9/', 'box': [543, 265, 560, 279]}, {'text': '3/', 'box': [560, 264, 575, 279]}, {'text': '92', 'box': [575, 264, 590, 279]}], 'id': 12}\n",
      "{'text': 'R&D Group:', 'box': [420, 310, 491, 323], 'linking': [[13, 4]], 'label': 'question', 'words': [{'text': 'R&D', 'box': [420, 310, 442, 323]}, {'text': 'Group:', 'box': [448, 310, 491, 323]}], 'id': 13}\n",
      "{'text': 'J. S. Wigand', 'box': [236, 313, 327, 327], 'linking': [[15, 14]], 'label': 'answer', 'words': [{'text': 'J.', 'box': [236, 313, 251, 327]}, {'text': 'S.', 'box': [256, 313, 273, 326]}, {'text': 'Wigand', 'box': [278, 313, 327, 327]}], 'id': 14}\n",
      "{'text': 'Supervisor / Manager', 'box': [91, 316, 218, 331], 'linking': [[15, 14]], 'label': 'question', 'words': [{'text': 'Supervisor', 'box': [91, 316, 161, 330]}, {'text': '/', 'box': [163, 318, 169, 331]}, {'text': 'Manager', 'box': [169, 317, 218, 327]}], 'id': 15}\n",
      "{'text': 'Discontinue coal retention analyses on licensee submitted product samples (Note : Coal Retention testing is not performed by most licensees. Other B&W physical measurements as ends stability and inspection for soft spots in ciparettes are thought to be sufficient measures to assure cigarette physical integrity. The proposed action will increase laboratory productivity . )', 'box': [190, 346, 594, 447], 'linking': [[2, 16]], 'label': 'answer', 'words': [{'text': 'Discontinue', 'box': [190, 355, 268, 366]}, {'text': 'coal', 'box': [274, 353, 303, 366]}, {'text': 'retention', 'box': [309, 352, 375, 365]}, {'text': 'analyses', 'box': [381, 351, 435, 365]}, {'text': 'on', 'box': [443, 352, 458, 363]}, {'text': 'licensee', 'box': [464, 348, 520, 362]}, {'text': 'submitted', 'box': [527, 346, 594, 361]}, {'text': 'product', 'box': [190, 369, 240, 383]}, {'text': 'samples', 'box': [247, 367, 301, 380]}, {'text': '(Note', 'box': [318, 365, 352, 379]}, {'text': ':', 'box': [352, 367, 359, 380]}, {'text': 'Coal', 'box': [373, 366, 402, 376]}, {'text': 'Retention', 'box': [408, 366, 472, 376]}, {'text': 'testing', 'box': [479, 365, 529, 376]}, {'text': 'is', 'box': [536, 363, 549, 374]}, {'text': 'not', 'box': [554, 363, 578, 374]}, {'text': 'performed', 'box': [190, 383, 256, 394]}, {'text': 'by', 'box': [261, 381, 275, 394]}, {'text': 'most', 'box': [282, 383, 311, 393]}, {'text': 'licensees.', 'box': [318, 380, 386, 391]}, {'text': 'Other', 'box': [401, 378, 437, 389]}, {'text': 'B&W', 'box': [443, 378, 465, 389]}, {'text': 'physical', 'box': [471, 377, 528, 391]}, {'text': 'measurements', 'box': [191, 398, 275, 406]}, {'text': 'as', 'box': [282, 397, 297, 405]}, {'text': 'ends', 'box': [304, 394, 332, 405]}, {'text': 'stability', 'box': [339, 394, 402, 405]}, {'text': 'and', 'box': [409, 392, 430, 402]}, {'text': 'inspection', 'box': [437, 392, 508, 403]}, {'text': 'for', 'box': [515, 391, 535, 402]}, {'text': 'soft', 'box': [542, 391, 571, 401]}, {'text': 'spots', 'box': [193, 411, 228, 422]}, {'text': 'in', 'box': [235, 409, 250, 420]}, {'text': 'ciparettes', 'box': [256, 409, 327, 419]}, {'text': 'are', 'box': [332, 408, 352, 418]}, {'text': 'thought', 'box': [360, 406, 410, 419]}, {'text': 'to', 'box': [415, 406, 430, 416]}, {'text': 'be', 'box': [436, 404, 453, 417]}, {'text': 'sufficient', 'box': [458, 405, 529, 415]}, {'text': 'measures', 'box': [535, 405, 592, 415]}, {'text': 'to', 'box': [193, 425, 208, 433]}, {'text': 'assure', 'box': [214, 423, 255, 431]}, {'text': 'cigarette', 'box': [261, 420, 325, 434]}, {'text': 'physical', 'box': [331, 419, 390, 432]}, {'text': 'integrity.', 'box': [395, 418, 463, 431]}, {'text': 'The', 'box': [478, 416, 500, 429]}, {'text': 'proposed', 'box': [506, 418, 566, 431]}, {'text': 'action', 'box': [193, 436, 236, 447]}, {'text': 'will', 'box': [240, 436, 269, 447]}, {'text': 'increase', 'box': [277, 434, 333, 445]}, {'text': 'laboratory', 'box': [339, 433, 410, 446]}, {'text': 'productivity', 'box': [418, 430, 502, 445]}, {'text': '.', 'box': [503, 433, 507, 444]}, {'text': ')', 'box': [508, 430, 514, 444]}], 'id': 16}\n",
      "{'text': 'Suggested Solutions (s) :', 'box': [95, 486, 250, 504], 'linking': [[17, 18]], 'label': 'question', 'words': [{'text': 'Suggested', 'box': [95, 489, 159, 504]}, {'text': 'Solutions', 'box': [165, 487, 222, 501]}, {'text': '(s)', 'box': [223, 486, 241, 503]}, {'text': ':', 'box': [243, 489, 250, 503]}], 'id': 17}\n",
      "{'text': 'Delete coal retention from the list of standard analyses performed on licensee submitted product samples. Special requests for coal retention testing could still be submitted on an exception basis.', 'box': [263, 483, 593, 553], 'linking': [[17, 18]], 'label': 'answer', 'words': [{'text': 'Delete', 'box': [263, 486, 306, 500]}, {'text': 'coal', 'box': [313, 486, 341, 499]}, {'text': 'retention', 'box': [348, 486, 412, 497]}, {'text': 'from', 'box': [416, 485, 447, 498]}, {'text': 'the', 'box': [453, 485, 475, 498]}, {'text': 'list', 'box': [480, 483, 508, 496]}, {'text': 'of', 'box': [515, 483, 532, 494]}, {'text': 'standard', 'box': [536, 483, 593, 494]}, {'text': 'analyses', 'box': [264, 501, 320, 514]}, {'text': 'performed', 'box': [324, 501, 392, 512]}, {'text': 'on', 'box': [397, 501, 412, 511]}, {'text': 'licensee', 'box': [419, 499, 475, 512]}, {'text': 'submitted', 'box': [482, 499, 546, 510]}, {'text': 'product', 'box': [264, 517, 314, 528]}, {'text': 'samples.', 'box': [320, 514, 374, 528]}, {'text': 'Special', 'box': [390, 513, 439, 526]}, {'text': 'requests', 'box': [446, 513, 502, 524]}, {'text': 'for', 'box': [508, 511, 530, 522]}, {'text': 'coal', 'box': [538, 510, 566, 523]}, {'text': 'retention', 'box': [263, 529, 330, 540]}, {'text': 'testing', 'box': [335, 527, 387, 540]}, {'text': 'could', 'box': [390, 527, 428, 538]}, {'text': 'still', 'box': [433, 525, 468, 536]}, {'text': 'be', 'box': [473, 525, 488, 535]}, {'text': 'submitted', 'box': [496, 524, 560, 537]}, {'text': 'on', 'box': [566, 524, 584, 537]}, {'text': 'an', 'box': [264, 543, 281, 553]}, {'text': 'exception', 'box': [286, 539, 350, 553]}, {'text': 'basis.', 'box': [355, 541, 397, 551]}], 'id': 18}\n",
      "{'text': 'Have you contacted your Manager/ Supervisor?', 'box': [96, 608, 398, 624], 'linking': [[19, 6], [19, 7]], 'label': 'header', 'words': [{'text': 'Have', 'box': [96, 612, 127, 623]}, {'text': 'you', 'box': [131, 613, 156, 624]}, {'text': 'contacted', 'box': [161, 612, 225, 623]}, {'text': 'your', 'box': [229, 610, 260, 623]}, {'text': 'Manager/', 'box': [264, 609, 314, 622]}, {'text': '', 'box': [314, 608, 322, 622]}, {'text': 'Supervisor?', 'box': [323, 608, 398, 621]}], 'id': 19}\n",
      "{'text': 'Manager Comments:', 'box': [98, 651, 211, 665], 'linking': [[20, 21], [20, 22]], 'label': 'question', 'words': [{'text': 'Manager', 'box': [98, 654, 150, 665]}, {'text': 'Comments:', 'box': [154, 651, 211, 664]}], 'id': 20}\n",
      "{'text': 'Manager, please contact suggester and forward', 'box': [232, 644, 547, 662], 'linking': [[20, 21]], 'label': 'answer', 'words': [{'text': 'Manager,', 'box': [232, 648, 288, 662]}, {'text': 'please', 'box': [296, 649, 338, 662]}, {'text': 'contact', 'box': [344, 648, 394, 662]}, {'text': 'suggester', 'box': [401, 648, 464, 661]}, {'text': 'and', 'box': [469, 647, 491, 658]}, {'text': 'forward', 'box': [497, 644, 547, 657]}], 'id': 21}\n",
      "{'text': 'comments to the Quality Council.', 'box': [99, 662, 323, 677], 'linking': [[20, 22]], 'label': 'answer', 'words': [{'text': 'comments', 'box': [99, 666, 155, 677]}, {'text': 'to', 'box': [162, 665, 177, 676]}, {'text': 'the', 'box': [183, 665, 205, 675]}, {'text': 'Quality', 'box': [211, 663, 261, 676]}, {'text': 'Council.', 'box': [267, 662, 323, 676]}], 'id': 22}\n",
      "{'text': 'qip . wp', 'box': [102, 823, 145, 838], 'linking': [], 'label': 'other', 'words': [{'text': 'qip', 'box': [102, 824, 123, 837]}, {'text': '.', 'box': [124, 824, 130, 838]}, {'text': 'wp', 'box': [130, 823, 145, 837]}], 'id': 23}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "with open(os.path.join(base_path, \"training_data/annotations/0000971160.json\")) as f:\n",
    "    data = json.load(f)\n",
    "\n",
    "for annotation in data[\"form\"]:\n",
    "    print(annotation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Hs4L3S5a2Gfb"
   },
   "source": [
    "The PIL library has a handy ImageDraw module, which -you guessed it- allows to draw things (such as rectangles) on an image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "gWaHFM_LtKPP",
    "outputId": "c498e560-035f-4170-b0b9-85ba3956711c"
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=762x1000>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "draw = ImageDraw.Draw(image, \"RGBA\")\n",
    "\n",
    "font = ImageFont.load_default()\n",
    "\n",
    "label2color = {\"question\": \"blue\", \"answer\": \"green\", \"header\": \"orange\", \"other\": \"violet\"}\n",
    "\n",
    "for annotation in data[\"form\"]:\n",
    "    label = annotation[\"label\"]\n",
    "    general_box = annotation[\"box\"]\n",
    "    draw.rectangle(general_box, outline=label2color[label], width=2)\n",
    "    draw.text((general_box[0] + 10, general_box[1] - 10), label, fill=label2color[label], font=font)\n",
    "    words = annotation[\"words\"]\n",
    "    for word in words:\n",
    "        box = word[\"box\"]\n",
    "        draw.rectangle(box, outline=label2color[label], width=1)\n",
    "\n",
    "image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uyWQNLSCRJN7"
   },
   "source": [
    "## Preprocessing the data\n",
    "\n",
    "Next, we need to turn the document images into individual tokens and corresponding labels (BIOES format, see further). We do this both for the training and test datasets. Make sure to run this from the `/content` directory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4DWRyOR9RuY6",
    "outputId": "4215a24b-8049-4b1a-a23f-5aaa48e14083"
   },
   "outputs": [],
   "source": [
    "! python unilm/layoutlm/deprecated/examples/seq_labeling/preprocess.py --data_dir data/training_data/annotations \\\n",
    "                                                      --data_split train \\\n",
    "                                                      --output_dir data \\\n",
    "                                                      --model_name_or_path microsoft/layoutlm-base-uncased \\\n",
    "                                                      --max_len 510\n",
    "\n",
    "! python unilm/layoutlm/deprecated/examples/seq_labeling/preprocess.py --data_dir data/testing_data/annotations \\\n",
    "                                                      --data_split test \\\n",
    "                                                      --output_dir data \\\n",
    "                                                      --model_name_or_path microsoft/layoutlm-base-uncased \\\n",
    "                                                      --max_len 510"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gc4Cu0ZyO5M_"
   },
   "source": [
    "Next, we create a labels.txt file that contains the unique labels of the FUNSD dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8iGOU0s3UR2u"
   },
   "outputs": [],
   "source": [
    "! cat data/train.txt | cut -d$'\\t' -f 2 | grep -v \"^$\"| sort | uniq > data/labels.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mC9FhkG9U8yg"
   },
   "source": [
    "## Define a PyTorch dataset\n",
    "\n",
    "First, we create a list containing the unique labels based on `data/labels.txt` (run this from the content directory):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "675rRa0QXnMp"
   },
   "outputs": [],
   "source": [
    "from torch.nn import CrossEntropyLoss\n",
    "\n",
    "\n",
    "def get_labels(path):\n",
    "    with open(path, \"r\") as f:\n",
    "        labels = f.read().splitlines()\n",
    "    if \"O\" not in labels:\n",
    "        labels = [\"O\"] + labels\n",
    "    return labels\n",
    "\n",
    "\n",
    "labels = get_labels(\"data/labels.txt\")\n",
    "num_labels = len(labels)\n",
    "label_map = {i: label for i, label in enumerate(labels)}\n",
    "# Use cross entropy ignore index as padding label id so that only real label ids contribute to the loss later\n",
    "pad_token_label_id = CrossEntropyLoss().ignore_index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kZ2LGEsez2u2"
   },
   "source": [
    "We can see that the dataset uses the so-called BIOES annotation scheme to annotate the tokens. This means that a given token can be either at the beginning (B), inside (I), outside (O), at the end (E) or start (S) of a given entity. Entities include ANSWER, QUESTION, HEADER and OTHER: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_-qXLkP9Yq_L",
    "outputId": "32ab46a4-4cf0-400c-816b-570f950035ec"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['B-ANSWER', 'B-HEADER', 'B-QUESTION', 'E-ANSWER', 'E-HEADER', 'E-QUESTION', 'I-ANSWER', 'I-HEADER', 'I-QUESTION', 'O', 'S-ANSWER', 'S-HEADER', 'S-QUESTION']\n"
     ]
    }
   ],
   "source": [
    "print(labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9_ck0ZFfZInR"
   },
   "source": [
    "Next, we can create a PyTorch dataset and corresponding dataloader (both for training and evaluation):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "\n",
    "import torch\n",
    "from torch.utils.data import Dataset\n",
    "\n",
    "logger = logging.getLogger(__name__)\n",
    "\n",
    "\n",
    "class FunsdDataset(Dataset):\n",
    "    def __init__(self, args, tokenizer, labels, pad_token_label_id, mode):\n",
    "        if args.local_rank not in [-1, 0] and mode == \"train\":\n",
    "            torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache\n",
    "\n",
    "        # Load data features from cache or dataset file\n",
    "        cached_features_file = os.path.join(\n",
    "            args.data_dir,\n",
    "            \"cached_{}_{}_{}\".format(\n",
    "                mode,\n",
    "                list(filter(None, args.model_name_or_path.split(\"/\"))).pop(),\n",
    "                str(args.max_seq_length),\n",
    "            ),\n",
    "        )\n",
    "        if os.path.exists(cached_features_file) and not args.overwrite_cache:\n",
    "            logger.info(\"Loading features from cached file %s\", cached_features_file)\n",
    "            features = torch.load(cached_features_file)\n",
    "        else:\n",
    "            logger.info(\"Creating features from dataset file at %s\", args.data_dir)\n",
    "            examples = read_examples_from_file(args.data_dir, mode)\n",
    "            features = convert_examples_to_features(\n",
    "                examples,\n",
    "                labels,\n",
    "                args.max_seq_length,\n",
    "                tokenizer,\n",
    "                cls_token_at_end=bool(args.model_type in [\"xlnet\"]),\n",
    "                # xlnet has a cls token at the end\n",
    "                cls_token=tokenizer.cls_token,\n",
    "                cls_token_segment_id=2 if args.model_type in [\"xlnet\"] else 0,\n",
    "                sep_token=tokenizer.sep_token,\n",
    "                sep_token_extra=bool(args.model_type in [\"roberta\"]),\n",
    "                # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805\n",
    "                pad_on_left=bool(args.model_type in [\"xlnet\"]),\n",
    "                # pad on the left for xlnet\n",
    "                pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],\n",
    "                pad_token_segment_id=4 if args.model_type in [\"xlnet\"] else 0,\n",
    "                pad_token_label_id=pad_token_label_id,\n",
    "            )\n",
    "            # if args.local_rank in [-1, 0]:\n",
    "            # logger.info(\"Saving features into cached file %s\", cached_features_file)\n",
    "            # torch.save(features, cached_features_file)\n",
    "\n",
    "        if args.local_rank == 0 and mode == \"train\":\n",
    "            torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache\n",
    "\n",
    "        self.features = features\n",
    "        # Convert to Tensors and build dataset\n",
    "        self.all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)\n",
    "        self.all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)\n",
    "        self.all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)\n",
    "        self.all_label_ids = torch.tensor([f.label_ids for f in features], dtype=torch.long)\n",
    "        self.all_bboxes = torch.tensor([f.boxes for f in features], dtype=torch.long)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.features)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        return (\n",
    "            self.all_input_ids[index],\n",
    "            self.all_input_mask[index],\n",
    "            self.all_segment_ids[index],\n",
    "            self.all_label_ids[index],\n",
    "            self.all_bboxes[index],\n",
    "        )\n",
    "\n",
    "\n",
    "class InputExample(object):\n",
    "    \"\"\"A single training/test example for token classification.\"\"\"\n",
    "\n",
    "    def __init__(self, guid, words, labels, boxes, actual_bboxes, file_name, page_size):\n",
    "        \"\"\"Constructs a InputExample.\n",
    "\n",
    "        Args:\n",
    "            guid: Unique id for the example.\n",
    "            words: list. The words of the sequence.\n",
    "            labels: (Optional) list. The labels for each word of the sequence. This should be\n",
    "            specified for train and dev examples, but not for test examples.\n",
    "        \"\"\"\n",
    "        self.guid = guid\n",
    "        self.words = words\n",
    "        self.labels = labels\n",
    "        self.boxes = boxes\n",
    "        self.actual_bboxes = actual_bboxes\n",
    "        self.file_name = file_name\n",
    "        self.page_size = page_size\n",
    "\n",
    "\n",
    "class InputFeatures(object):\n",
    "    \"\"\"A single set of features of data.\"\"\"\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        input_ids,\n",
    "        input_mask,\n",
    "        segment_ids,\n",
    "        label_ids,\n",
    "        boxes,\n",
    "        actual_bboxes,\n",
    "        file_name,\n",
    "        page_size,\n",
    "    ):\n",
    "        assert (\n",
    "            0 <= all(boxes) <= 1000\n",
    "        ), \"Error with input bbox ({}): the coordinate value is not between 0 and 1000\".format(boxes)\n",
    "        self.input_ids = input_ids\n",
    "        self.input_mask = input_mask\n",
    "        self.segment_ids = segment_ids\n",
    "        self.label_ids = label_ids\n",
    "        self.boxes = boxes\n",
    "        self.actual_bboxes = actual_bboxes\n",
    "        self.file_name = file_name\n",
    "        self.page_size = page_size\n",
    "\n",
    "\n",
    "def read_examples_from_file(data_dir, mode):\n",
    "    file_path = os.path.join(data_dir, \"{}.txt\".format(mode))\n",
    "    box_file_path = os.path.join(data_dir, \"{}_box.txt\".format(mode))\n",
    "    image_file_path = os.path.join(data_dir, \"{}_image.txt\".format(mode))\n",
    "    guid_index = 1\n",
    "    examples = []\n",
    "    with open(file_path, encoding=\"utf-8\") as f, open(box_file_path, encoding=\"utf-8\") as fb, open(\n",
    "        image_file_path, encoding=\"utf-8\"\n",
    "    ) as fi:\n",
    "        words = []\n",
    "        boxes = []\n",
    "        actual_bboxes = []\n",
    "        file_name = None\n",
    "        page_size = None\n",
    "        labels = []\n",
    "        for line, bline, iline in zip(f, fb, fi):\n",
    "            if line.startswith(\"-DOCSTART-\") or line == \"\" or line == \"\\n\":\n",
    "                if words:\n",
    "                    examples.append(\n",
    "                        InputExample(\n",
    "                            guid=\"{}-{}\".format(mode, guid_index),\n",
    "                            words=words,\n",
    "                            labels=labels,\n",
    "                            boxes=boxes,\n",
    "                            actual_bboxes=actual_bboxes,\n",
    "                            file_name=file_name,\n",
    "                            page_size=page_size,\n",
    "                        )\n",
    "                    )\n",
    "                    guid_index += 1\n",
    "                    words = []\n",
    "                    boxes = []\n",
    "                    actual_bboxes = []\n",
    "                    file_name = None\n",
    "                    page_size = None\n",
    "                    labels = []\n",
    "            else:\n",
    "                splits = line.split(\"\\t\")\n",
    "                bsplits = bline.split(\"\\t\")\n",
    "                isplits = iline.split(\"\\t\")\n",
    "                assert len(splits) == 2\n",
    "                assert len(bsplits) == 2\n",
    "                assert len(isplits) == 4\n",
    "                assert splits[0] == bsplits[0]\n",
    "                words.append(splits[0])\n",
    "                if len(splits) > 1:\n",
    "                    labels.append(splits[-1].replace(\"\\n\", \"\"))\n",
    "                    box = bsplits[-1].replace(\"\\n\", \"\")\n",
    "                    box = [int(b) for b in box.split()]\n",
    "                    boxes.append(box)\n",
    "                    actual_bbox = [int(b) for b in isplits[1].split()]\n",
    "                    actual_bboxes.append(actual_bbox)\n",
    "                    page_size = [int(i) for i in isplits[2].split()]\n",
    "                    file_name = isplits[3].strip()\n",
    "                else:\n",
    "                    # Examples could have no label for mode = \"test\"\n",
    "                    labels.append(\"O\")\n",
    "        if words:\n",
    "            examples.append(\n",
    "                InputExample(\n",
    "                    guid=\"%s-%d\".format(mode, guid_index),\n",
    "                    words=words,\n",
    "                    labels=labels,\n",
    "                    boxes=boxes,\n",
    "                    actual_bboxes=actual_bboxes,\n",
    "                    file_name=file_name,\n",
    "                    page_size=page_size,\n",
    "                )\n",
    "            )\n",
    "    return examples\n",
    "\n",
    "\n",
    "def convert_examples_to_features(\n",
    "    examples,\n",
    "    label_list,\n",
    "    max_seq_length,\n",
    "    tokenizer,\n",
    "    cls_token_at_end=False,\n",
    "    cls_token=\"[CLS]\",\n",
    "    cls_token_segment_id=1,\n",
    "    sep_token=\"[SEP]\",\n",
    "    sep_token_extra=False,\n",
    "    pad_on_left=False,\n",
    "    pad_token=0,\n",
    "    cls_token_box=[0, 0, 0, 0],\n",
    "    sep_token_box=[1000, 1000, 1000, 1000],\n",
    "    pad_token_box=[0, 0, 0, 0],\n",
    "    pad_token_segment_id=0,\n",
    "    pad_token_label_id=-1,\n",
    "    sequence_a_segment_id=0,\n",
    "    mask_padding_with_zero=True,\n",
    "):\n",
    "    \"\"\"Loads a data file into a list of `InputBatch`s\n",
    "    `cls_token_at_end` define the location of the CLS token:\n",
    "        - False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP]\n",
    "        - True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]\n",
    "    `cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet)\n",
    "    \"\"\"\n",
    "\n",
    "    label_map = {label: i for i, label in enumerate(label_list)}\n",
    "\n",
    "    features = []\n",
    "    for ex_index, example in enumerate(examples):\n",
    "        file_name = example.file_name\n",
    "        page_size = example.page_size\n",
    "        width, height = page_size\n",
    "        if ex_index % 10000 == 0:\n",
    "            logger.info(\"Writing example %d of %d\", ex_index, len(examples))\n",
    "\n",
    "        tokens = []\n",
    "        token_boxes = []\n",
    "        actual_bboxes = []\n",
    "        label_ids = []\n",
    "        for word, label, box, actual_bbox in zip(example.words, example.labels, example.boxes, example.actual_bboxes):\n",
    "            word_tokens = tokenizer.tokenize(word)\n",
    "            tokens.extend(word_tokens)\n",
    "            token_boxes.extend([box] * len(word_tokens))\n",
    "            actual_bboxes.extend([actual_bbox] * len(word_tokens))\n",
    "            # Use the real label id for the first token of the word, and padding ids for the remaining tokens\n",
    "            label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(word_tokens) - 1))\n",
    "\n",
    "        # Account for [CLS] and [SEP] with \"- 2\" and with \"- 3\" for RoBERTa.\n",
    "        special_tokens_count = 3 if sep_token_extra else 2\n",
    "        if len(tokens) > max_seq_length - special_tokens_count:\n",
    "            tokens = tokens[: (max_seq_length - special_tokens_count)]\n",
    "            token_boxes = token_boxes[: (max_seq_length - special_tokens_count)]\n",
    "            actual_bboxes = actual_bboxes[: (max_seq_length - special_tokens_count)]\n",
    "            label_ids = label_ids[: (max_seq_length - special_tokens_count)]\n",
    "\n",
    "        # The convention in BERT is:\n",
    "        # (a) For sequence pairs:\n",
    "        #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]\n",
    "        #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1\n",
    "        # (b) For single sequences:\n",
    "        #  tokens:   [CLS] the dog is hairy . [SEP]\n",
    "        #  type_ids:   0   0   0   0  0     0   0\n",
    "        #\n",
    "        # Where \"type_ids\" are used to indicate whether this is the first\n",
    "        # sequence or the second sequence. The embedding vectors for `type=0` and\n",
    "        # `type=1` were learned during pre-training and are added to the wordpiece\n",
    "        # embedding vector (and position vector). This is not *strictly* necessary\n",
    "        # since the [SEP] token unambiguously separates the sequences, but it makes\n",
    "        # it easier for the model to learn the concept of sequences.\n",
    "        #\n",
    "        # For classification tasks, the first vector (corresponding to [CLS]) is\n",
    "        # used as as the \"sentence vector\". Note that this only makes sense because\n",
    "        # the entire model is fine-tuned.\n",
    "        tokens += [sep_token]\n",
    "        token_boxes += [sep_token_box]\n",
    "        actual_bboxes += [[0, 0, width, height]]\n",
    "        label_ids += [pad_token_label_id]\n",
    "        if sep_token_extra:\n",
    "            # roberta uses an extra separator b/w pairs of sentences\n",
    "            tokens += [sep_token]\n",
    "            token_boxes += [sep_token_box]\n",
    "            actual_bboxes += [[0, 0, width, height]]\n",
    "            label_ids += [pad_token_label_id]\n",
    "        segment_ids = [sequence_a_segment_id] * len(tokens)\n",
    "\n",
    "        if cls_token_at_end:\n",
    "            tokens += [cls_token]\n",
    "            token_boxes += [cls_token_box]\n",
    "            actual_bboxes += [[0, 0, width, height]]\n",
    "            label_ids += [pad_token_label_id]\n",
    "            segment_ids += [cls_token_segment_id]\n",
    "        else:\n",
    "            tokens = [cls_token] + tokens\n",
    "            token_boxes = [cls_token_box] + token_boxes\n",
    "            actual_bboxes = [[0, 0, width, height]] + actual_bboxes\n",
    "            label_ids = [pad_token_label_id] + label_ids\n",
    "            segment_ids = [cls_token_segment_id] + segment_ids\n",
    "\n",
    "        input_ids = tokenizer.convert_tokens_to_ids(tokens)\n",
    "\n",
    "        # The mask has 1 for real tokens and 0 for padding tokens. Only real\n",
    "        # tokens are attended to.\n",
    "        input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)\n",
    "\n",
    "        # Zero-pad up to the sequence length.\n",
    "        padding_length = max_seq_length - len(input_ids)\n",
    "        if pad_on_left:\n",
    "            input_ids = ([pad_token] * padding_length) + input_ids\n",
    "            input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask\n",
    "            segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids\n",
    "            label_ids = ([pad_token_label_id] * padding_length) + label_ids\n",
    "            token_boxes = ([pad_token_box] * padding_length) + token_boxes\n",
    "        else:\n",
    "            input_ids += [pad_token] * padding_length\n",
    "            input_mask += [0 if mask_padding_with_zero else 1] * padding_length\n",
    "            segment_ids += [pad_token_segment_id] * padding_length\n",
    "            label_ids += [pad_token_label_id] * padding_length\n",
    "            token_boxes += [pad_token_box] * padding_length\n",
    "\n",
    "        assert len(input_ids) == max_seq_length\n",
    "        assert len(input_mask) == max_seq_length\n",
    "        assert len(segment_ids) == max_seq_length\n",
    "        assert len(label_ids) == max_seq_length\n",
    "        assert len(token_boxes) == max_seq_length\n",
    "\n",
    "        if ex_index < 5:\n",
    "            logger.info(\"*** Example ***\")\n",
    "            logger.info(\"guid: %s\", example.guid)\n",
    "            logger.info(\"tokens: %s\", \" \".join([str(x) for x in tokens]))\n",
    "            logger.info(\"input_ids: %s\", \" \".join([str(x) for x in input_ids]))\n",
    "            logger.info(\"input_mask: %s\", \" \".join([str(x) for x in input_mask]))\n",
    "            logger.info(\"segment_ids: %s\", \" \".join([str(x) for x in segment_ids]))\n",
    "            logger.info(\"label_ids: %s\", \" \".join([str(x) for x in label_ids]))\n",
    "            logger.info(\"boxes: %s\", \" \".join([str(x) for x in token_boxes]))\n",
    "            logger.info(\"actual_bboxes: %s\", \" \".join([str(x) for x in actual_bboxes]))\n",
    "\n",
    "        features.append(\n",
    "            InputFeatures(\n",
    "                input_ids=input_ids,\n",
    "                input_mask=input_mask,\n",
    "                segment_ids=segment_ids,\n",
    "                label_ids=label_ids,\n",
    "                boxes=token_boxes,\n",
    "                actual_bboxes=actual_bboxes,\n",
    "                file_name=file_name,\n",
    "                page_size=page_size,\n",
    "            )\n",
    "        )\n",
    "    return features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "HUJftzeBWh2S"
   },
   "outputs": [],
   "source": [
    "from transformers import LayoutLMTokenizer\n",
    "\n",
    "# from .unilm.layoutlm.data.funsd import FunsdDataset, InputFeatures\n",
    "from torch.utils.data import DataLoader, RandomSampler, SequentialSampler\n",
    "\n",
    "batch_size = 16\n",
    "args = {\n",
    "    \"local_rank\": -1,\n",
    "    \"overwrite_cache\": True,\n",
    "    \"data_dir\": \"data/\",\n",
    "    \"model_name_or_path\": \"microsoft/layoutlm-base-uncased\",\n",
    "    \"max_seq_length\": 512,\n",
    "    \"model_type\": \"layoutlm\",\n",
    "}\n",
    "\n",
    "\n",
    "# class to turn the keys of a dict into attributes (thanks Stackoverflow)\n",
    "class AttrDict(dict):\n",
    "    def __init__(self, *args, **kwargs):\n",
    "        super(AttrDict, self).__init__(*args, **kwargs)\n",
    "        self.__dict__ = self\n",
    "\n",
    "\n",
    "args = AttrDict(args)\n",
    "\n",
    "tokenizer = LayoutLMTokenizer.from_pretrained(\"microsoft/layoutlm-base-uncased\")\n",
    "\n",
    "# the LayoutLM authors already defined a specific FunsdDataset, so we are going to use this here\n",
    "train_dataset = FunsdDataset(args, tokenizer, labels, pad_token_label_id, mode=\"train\")\n",
    "train_sampler = RandomSampler(train_dataset)\n",
    "train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=batch_size)\n",
    "\n",
    "eval_dataset = FunsdDataset(args, tokenizer, labels, pad_token_label_id, mode=\"test\")\n",
    "eval_sampler = SequentialSampler(eval_dataset)\n",
    "eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "18NMUBzgOdqu",
    "outputId": "eef47b70-3a9a-4b19-be6b-95900c58337b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_dataloader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "toFjxtn71B1U",
    "outputId": "f4651896-cafc-449a-98b4-c81f41177e6d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(eval_dataloader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 137
    },
    "id": "RhINSBw9I24G",
    "outputId": "28738ce2-617c-47d3-b8c9-f949d3066d60"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[CLS] account agency ss : date 1 / 31 / 88 insert signed 91581919 4th new albany tribune advertisements. bookkeeper mar 08 reco affidavit of performance from newspaper new albany tribune harley davidson cigarettes lorillard media service state of county of indiana ) floyd ) before me a notary public, personally appeared holly inzer who being duly sworn, says that ( he ) ( she ) is of the abovementioned newspaper and that display ads for the above account were made through the aforesaid newspaper during the month of january, 1988 as follows : column inches exclusive advertising for harley davidson cigarettes we hereby certify charges shown above on dates per attached bill are true and correct as billed to the account in upper right hand corner of the affidavit and are exclusive sworn to and subscribed before me this day of march, 1988 in testimony whereof i have set my hand and seal the day and year aforesaid. my commission expires : 2 - 9 - 90 betty j. murphy ( notary public ) [SEP] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch = next(iter(train_dataloader))\n",
    "input_ids = batch[0][0]\n",
    "tokenizer.decode(input_ids)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "66cEmLDoUFcm"
   },
   "source": [
    "## Define and fine-tune the model\n",
    "\n",
    "As this is a sequence labeling task, we are going to load `LayoutLMForTokenClassification` (the base sized model) from the hub. We are going to fine-tune it on a downstream task, namely FUNSD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LoraConfig(task_type=<TaskType.TOKEN_CLS: 'TOKEN_CLS'>, peft_type=<PeftType.LORA: 'LORA'>, auto_mapping=None, base_model_name_or_path=None, revision=None, inference_mode=False, r=16, target_modules=None, exclude_modules=None, lora_alpha=16, lora_dropout=0.1, fan_in_fan_out=False, bias='all', use_rslora=False, modules_to_save=None, init_lora_weights=True, layers_to_transform=None, layers_pattern=None, rank_pattern={}, alpha_pattern={}, megatron_config=None, megatron_core='megatron.core', trainable_token_indices=None, loftq_config={}, eva_config=None, corda_config=None, use_dora=False, use_qalora=False, qalora_group_size=16, layer_replication=None, runtime_config=LoraRuntimeConfig(ephemeral_gpu_offload=False), lora_bias=False, target_parameters=None)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from peft import get_peft_config, PeftModel, get_peft_model, LoraConfig, TaskType\n",
    "\n",
    "peft_config = LoraConfig(\n",
    "    task_type=TaskType.TOKEN_CLS, inference_mode=False, r=16, lora_alpha=16, lora_dropout=0.1, bias=\"all\"\n",
    ")\n",
    "peft_config"
   ]
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   "metadata": {
    "colab": {
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    "outputId": "95e8811c-025a-41a0-9d03-4285a17f2a9b"
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   "outputs": [],
   "source": [
    "from transformers import LayoutLMForTokenClassification\n",
    "import torch\n",
    "from transformers import set_seed\n",
    "\n",
    "seed = 100\n",
    "set_seed(seed)\n",
    "device = torch.accelerator.current_accelerator().type if hasattr(torch, \"accelerator\") else \"cuda\"\n",
    "\n",
    "model = LayoutLMForTokenClassification.from_pretrained(\"microsoft/layoutlm-base-uncased\", num_labels=num_labels)\n",
    "model = get_peft_model(model, peft_config)\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(model.model.layoutlm.encoder.layer[0].attention.self.query.weight)\n",
    "print(model.model.layoutlm.encoder.layer[0].attention.self.query.lora_A.default.weight)\n",
    "print(model.model.classifier.weight)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3weFr_pz1mla"
   },
   "source": [
    "Now we can start training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Yu0qePs2cRKo",
    "outputId": "cdbb9a03-eb9b-4740-bbe3-da06b9192bae"
   },
   "outputs": [],
   "source": [
    "from transformers import get_linear_schedule_with_warmup\n",
    "from tqdm import tqdm\n",
    "\n",
    "num_train_epochs = 100\n",
    "\n",
    "optimizer = torch.optim.AdamW(model.parameters(), lr=3e-3)\n",
    "lr_scheduler = get_linear_schedule_with_warmup(\n",
    "    optimizer=optimizer,\n",
    "    num_warmup_steps=0.06 * (len(train_dataloader) * num_train_epochs),\n",
    "    num_training_steps=(len(train_dataloader) * num_train_epochs),\n",
    ")\n",
    "\n",
    "\n",
    "global_step = 0\n",
    "\n",
    "t_total = len(train_dataloader) * num_train_epochs  # total number of training steps\n",
    "\n",
    "# put the model in training mode\n",
    "model.train()\n",
    "for epoch in range(num_train_epochs):\n",
    "    for batch in tqdm(train_dataloader, desc=\"Training\"):\n",
    "        input_ids = batch[0].to(device)\n",
    "        bbox = batch[4].to(device)\n",
    "        attention_mask = batch[1].to(device)\n",
    "        token_type_ids = batch[2].to(device)\n",
    "        labels = batch[3].to(device)\n",
    "\n",
    "        # forward pass\n",
    "        outputs = model(\n",
    "            input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids, labels=labels\n",
    "        )\n",
    "        loss = outputs.loss\n",
    "\n",
    "        # print loss every 100 steps\n",
    "        if global_step % 10 == 0:\n",
    "            print(f\"Loss after {global_step} steps: {loss.item()}\")\n",
    "\n",
    "        # backward pass to get the gradients\n",
    "        loss.backward()\n",
    "\n",
    "        # print(\"Gradients on classification head:\")\n",
    "        # print(model.classifier.weight.grad[6,:].sum())\n",
    "\n",
    "        # update\n",
    "        optimizer.step()\n",
    "        lr_scheduler.step()\n",
    "        optimizer.zero_grad()\n",
    "        global_step += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "u1rNslap5Y3N",
    "outputId": "877183d4-1d29-4d09-bd3a-0e5f88611dc8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluating: 100%|██████████| 4/4 [00:00<00:00, 10.90it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'loss': 1.467050313949585, 'precision': 0.7295341474445952, 'recall': 0.806903451725863, 'f1': 0.7662707838479811}\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from seqeval.metrics import (\n",
    "    classification_report,\n",
    "    f1_score,\n",
    "    precision_score,\n",
    "    recall_score,\n",
    ")\n",
    "\n",
    "eval_loss = 0.0\n",
    "nb_eval_steps = 0\n",
    "preds = None\n",
    "out_label_ids = None\n",
    "\n",
    "# put model in evaluation mode\n",
    "model.eval()\n",
    "for batch in tqdm(eval_dataloader, desc=\"Evaluating\"):\n",
    "    with torch.no_grad():\n",
    "        input_ids = batch[0].to(device)\n",
    "        bbox = batch[4].to(device)\n",
    "        attention_mask = batch[1].to(device)\n",
    "        token_type_ids = batch[2].to(device)\n",
    "        labels = batch[3].to(device)\n",
    "\n",
    "        # forward pass\n",
    "        outputs = model(\n",
    "            input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids, labels=labels\n",
    "        )\n",
    "        # get the loss and logits\n",
    "        tmp_eval_loss = outputs.loss\n",
    "        logits = outputs.logits\n",
    "\n",
    "        eval_loss += tmp_eval_loss.item()\n",
    "        nb_eval_steps += 1\n",
    "\n",
    "        # compute the predictions\n",
    "        if preds is None:\n",
    "            preds = logits.detach().cpu().numpy()\n",
    "            out_label_ids = labels.detach().cpu().numpy()\n",
    "        else:\n",
    "            preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)\n",
    "            out_label_ids = np.append(out_label_ids, labels.detach().cpu().numpy(), axis=0)\n",
    "\n",
    "# compute average evaluation loss\n",
    "eval_loss = eval_loss / nb_eval_steps\n",
    "preds = np.argmax(preds, axis=2)\n",
    "\n",
    "out_label_list = [[] for _ in range(out_label_ids.shape[0])]\n",
    "preds_list = [[] for _ in range(out_label_ids.shape[0])]\n",
    "\n",
    "for i in range(out_label_ids.shape[0]):\n",
    "    for j in range(out_label_ids.shape[1]):\n",
    "        if out_label_ids[i, j] != pad_token_label_id:\n",
    "            out_label_list[i].append(label_map[out_label_ids[i][j]])\n",
    "            preds_list[i].append(label_map[preds[i][j]])\n",
    "\n",
    "results = {\n",
    "    \"loss\": eval_loss,\n",
    "    \"precision\": precision_score(out_label_list, preds_list),\n",
    "    \"recall\": recall_score(out_label_list, preds_list),\n",
    "    \"f1\": f1_score(out_label_list, preds_list),\n",
    "}\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainable params: 702,733 || all params: 113,237,786 || trainable%: 0.6206\n"
     ]
    }
   ],
   "source": [
    "model.print_trainable_parameters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save_pretrained(\"peft_layoutlm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.8M\t./peft_layoutlm/adapter_model.safetensors\n"
     ]
    }
   ],
   "source": [
    "!du -h ./peft_layoutlm/adapter_model.safetensors"
   ]
  }
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